Remote | DevOps Engineer — Up to $80/hour

24-Mag • New York, NY

1d • Remote

About The Position

We are sharing a specialised part-time consulting opportunity for experienced DevOps, Site Reliability, and Cloud Engineering professionals with hands-on expertise in production infrastructure, cloud platforms, Kubernetes, CI/CD, observability, and infrastructure automation. This sprint-based role supports an advanced AI research initiative focused on evaluating frontier coding models through realistic infrastructure engineering workflows. Selected professionals will use AI coding agents to complete technical tasks, review model-generated infrastructure implementations, identify reliability and engineering failures, and compare how different models perform across practical DevOps, SRE, and cloud scenarios.

Requirements

• At least 2 years of professional DevOps, Site Reliability Engineering, or Cloud Engineering experience
• Hands-on experience supporting production-scale infrastructure or distributed systems
• Experience with AWS, Azure, GCP, Kubernetes, Terraform, CI/CD pipelines, or observability tooling
• Regular use of AI coding agents within technical workflows
• Strong ability to evaluate model-generated infrastructure and reliability engineering solutions
• Experience diagnosing production issues, deployment failures, and infrastructure problems
• Strong technical judgment, debugging skills, and written communication
• Ability to work efficiently within short, intensive project sprints

Nice To Haves

• Experience with AWS, Azure, or Google Cloud Platform
• Strong Kubernetes and container orchestration experience
• Expertise with Terraform or comparable infrastructure-as-code tooling
• Background designing or maintaining CI/CD pipelines
• Familiarity with observability platforms, monitoring, logging, and incident response
• Experience with Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or comparable AI coding tools
• Knowledge of production reliability, scalability, disaster recovery, and performance engineering
• Previous exposure to AI evaluation, benchmark development, or structured technical review
• A degree in computer science, software engineering, information technology, cloud computing, or a related technical discipline may be helpful
• Advanced technical training in cloud infrastructure, systems engineering, networking, or DevOps may strengthen an application
• Relevant cloud or infrastructure certifications may also be valuable
• Equivalent professional experience supporting production systems may be considered

Responsibilities

• Review complex infrastructure engineering tasks completed with frontier AI coding agents
• Evaluate implementations involving cloud platforms, Kubernetes, CI/CD systems, observability, and infrastructure automation
• Assess technical correctness, reliability, maintainability, and operational readiness
• Apply professional engineering judgment to realistic production infrastructure scenarios
• Use frontier AI coding agents within practical infrastructure engineering workflows
• Evaluate how effectively coding models interpret requirements and implement solutions
• Identify bugs, edge cases, reliability issues, configuration errors, and failure modes
• Assess where models require correction, additional prompting, or manual engineering intervention
• Evaluate solutions involving AWS, Azure, GCP, or comparable cloud environments
• Review Kubernetes configurations, deployment workflows, and infrastructure orchestration
• Assess Terraform or similar infrastructure-as-code implementations
• Review CI/CD pipelines, monitoring, logging, alerting, and observability approaches
• Identify security, scalability, resilience, and operational concerns where relevant
• Compare infrastructure solutions produced by multiple frontier coding models
• Assess differences in implementation strategy, technical reasoning, reliability, and code quality
• Determine which approaches best satisfy task requirements
• Document model strengths, weaknesses, and recurring engineering failure patterns
• Provide clear written assessments explaining relevant technical trade-offs

Benefits

• Flexible scheduling
• Task-based compensation
• Weekly payments via Stripe or Wise
• Projects may be extended, shortened, or adjusted depending on scope and performance

What This Job Offers

• Job Type: Part-time
• Career Level: Mid Level
• Education Level: No Education Listed